class_docstr: Augmenter that leverage contextual word embeddings to find top n similar word for augmentation.
  
  :param str model_dir: Directory of model. It is generated from train_lambada.sh under scritps folders.n
  :param float threshold: The threshold of classification probabilty for accpeting generated text. Return all result if threshold
      is None.
  :param int batch_size: Batch size.
  :param int min_length: The min length of output text.
  :param int max_length: The max length of output text.
  :param float temperature: The value used to module the next token probabilities.
  :param int top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering.
  :param float top_p: If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or
      higher are kept for generation.
  :param float repetition_penalty : The parameter for repetition penalty. 1.0 means no penalty.
  :param str device: Default value is CPU. If value is CPU, it uses CPU for processing. If value is CUDA, it uses GPU
      for processing. Possible values include 'cuda' and 'cpu'. 
  :param bool force_reload: Force reload the contextual word embeddings model to memory when initialize the class.
      Default value is False and suggesting to keep it as False if performance is the consideration.
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.sentence as nas
  >>> aug = nas.LambadaAug()
code: def __init__(
      self,
      model_dir,
      threshold=None,
      min_length=100,
      max_length=300,
      batch_size=16,
      temperature=1.0,
      top_k=50,
      top_p=0.9,
      repetition_penalty=1.0,
      name="Lambada_Aug",
      device="cpu",
      force_reload=False,
      verbose=0,
  ):
      super().__init__(
          action=Action.INSERT,
          name=name,
          tokenizer=None,
          stopwords=None,
          device=device,
          include_detail=False,
          verbose=verbose,
      )
  
      self.model_dir = model_dir
  
      with open(
          os.path.join(model_dir, "cls", "label_encoder.json")
      ) as json_file:
          self.label2id = json.load(json_file)
  
      self.model = self.get_model(
          model_dir=model_dir,
          threshold=threshold,
          max_length=max_length,
          min_length=min_length,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
          repetition_penalty=repetition_penalty,
          batch_size=batch_size,
          device=device,
          force_reload=force_reload,
      )
      self.device = self.model.get_device()
  
file_path: nlpaug/nlpaug/augmenter/sentence/lambada.py
imports: import os
  import json
  from nlpaug.augmenter.sentence import SentenceAugmenter
  import nlpaug.model.lang_models
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 1 to create a new LambadaAug object from inherited base classes: SentenceAugmenter with example: obj = LambadaAug(model_dir, threshold, min_length, max_length, batch_size, temperature, top_k, top_p, repetition_penalty, name, device, force_reload, verbose)